stocks of tropical forests Ed Mitchard University of Edinburgh - PowerPoint PPT Presentation

Mapping the changing carbon stocks of tropical forests Ed Mitchard University of Edinburgh edward.mitchard@ed.ac.uk Oxford Centre for Tropical Forests, 2 nd November 2018

Massive uncertainty in in tropical carbon cycle Carbon flux from tropics to atmosphere: Caused by uncertainty and disagreements in -2.2 - +2.35 Pg C/year data from different methods (modelling, field studies, atmospheric measurements, different types of satellite data)  negative for REDD+ and climate modelling Deforestation Tree growth & degradation 0.9 – 2.85 0.5 – 3.1 Pg C/year Pg C/year Tropical Aboveground Forest Carbon Stocks 170 – 280 Pg C (95 % C.I.) Mitchard, E. T. A. 2018. The tropical forest carbon cycle and climate 2 change. Nature 559 527-534

Why use satellite data to map tropical forest carbon stocks? • Ground plots sparse & expensive • Cannot do enough! • Cannot access all areas • Need broad scale with timely repeats • Consistency

Optical satellite data • About 500 satellites in orbit! • Lots of data, mostly free • Has been used to make maps of forest cover and forest change (deforestation) • Hansen et al (2013) (Global Forest Watch) – Forest loss from 2000- 2017, based on analysis of 1 million Landsat scenes • PRODES – Deforestation mapped annually across Brazil

Issues with using conventional (optical) satellite data to monitor forests 1. Cloud cover 2. Only see the top of the canopy – biomass canopy cover 3. Spectral inconsistencies

The world as seen from the DSCOVR satellite May 2016

Data from MODIS L2 Cloud Mask Product (1km resolution)

1. Pristine Budongo Forest, Uganda 2. Degraded

Greeness relates to canopy cover, not biomass Normalised Difference 0.55 Vegetation Index (NDVI) 0.50 NDVI = (NIR – RED) ASTER 2006 NDVI 0.45 (NIR + RED) 0.40 0.35 Reflectance (%) 0.30 0.25 0 1 2 3 4 Canopy cover (m 2 /m 2 ) Mitchard, E. T. A., et al. 2009. Measuring woody encroachment from 1982-2006 along a forest-savanna boundary in central Africa. Earth Interactions , 13, 1-29. Wave Length (nm)

Inconsistent spectral signatures • Grass, crops, secondary vegetation and trees are all green at some point in the year • Subtler changes occur too within forests, relating to leaf Wet season age/phenology/flowers • Changes too due to atmospheric conditions, satellite platforms Dry season

Types of remote sensing data Optical Lidar Radar

LiDAR can image whole trees

LiDAR can map tree height, canopy size -> bio iomass • LiDAR-estimated mean or top-of- canopy height relate strongly to biomass within field plots, without saturation • Can also use individual tree recognition methods • Depends on use of locally-derived diameter:height relationships and wood density Asner et al. 2010. High-resolution forest carbon stocks and emissions in the Amazon. PNAS 107: 16738-16742

Bio iomass = f( f(Dia iameter, wood density, height) Height LiDAR measures Wood density Diameter height only

LiDAR Problems • Data volumes large, processing tricky & site specific • Continuous-cover data is from aircraft/UAV only • Expensive • Rare • Non-operational • Satellite data is dispersed footprints • Average figures over many trees • Still rare – sampling tool • Cloud cover a problem ICESat Orbits (footprints along tracks 170 m apart, 70 m diameter)

Satellite LiDAR has been used to make pantropical biomass maps Tens of Spaceborne Spatial Ground thousands of LiDAR data Averaging plots point biomass (ICESat GLAS) estimates Tens of Full coverage thousands of Spatial optical (MODIS) Modelling AGB maps point biomass and elevation estimates (SRTM) data Testing

Baccini et al Saatchi et al (2012, Nature (2011, PNAS ) Climate Change )

Unfortunately these biomass maps differ quite a bit Baccini et al. (2012) AGB (Mg ha -1 ) 0 100 200 300 400 500 Saatchi et al. (2011) AGB (Mg ha -1 ) 0 100 200 300 400 500 Absolute Difference AGB (Mg ha -1 ) -150 -100 -50 0 +50 +100 +150

Why do they differ? Do either match reality? Diameter-height relationships Wood density Ter Steege et al. 2006 Nature Feldpausch et al. 2011 Biogeosciences

Field plots show a different pattern to both maps over Amazonia AGB Kriged from plots Saatchi Baccini (Mg ha -1 ) 0 413 plots 100 200 300 400 500 AGB difference (Mg ha -1 ) Baccini-Krig Saatchi-Baccini Saatchi-Krig -200 -100 0 100 200 Mitchard et al. 2014. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Global Ecology & Biogeography

Must consider spatial variation in wood density and forest structure ≠ ≠ DBH: 30 cm DBH: 100 cm Density: Density: H: 25 m H: 25 m 0.9 0.45

Conclusion on Optical & Lidar • Optical data good for mapping canopy cover (e.g. forest area, deforestation) • But cloud cover • But inconsistent in time • LiDAR data good for biomass • But height only – biases • Data limited • Combined Optical+LiDAR made the first pantropical biomas maps ≠ ≠ • But biases • But limited to mid-2000s  GEDI & ICESat-2 launches mean new DBH: 30 cm DBH: 100 cm Density: Density: satellite data from 2018/19 H: 25 m H: 25 m 0.9 0.45

There is another technology… Optical Lidar Radar

Radar

Radar • Side-looking • Signal ‘scattered’ Active from trunks and Return radar signal branches pulse (backscatter)

Optical vs. Radar

Radar interactions with forest structure (H,V) (H,V)  ฀ Backscatter Measurements:  ( H , V )   crown   stem   ground  ( H , V )  f ( vol , W d ,  ) vol : forest volume (size) W d : wood density (dielectric constant) ฀  : shape and orientation of components ฀

Radar backscatter is Cameroon - MD Mozambique - NC sensitive to biomass Mozambique - NN Uganda - BFR HV backscatter vs AGB HV backscatter vs log AGB -9 -9 -10 -10 -11 -11 ALOS HV    (dB) ALOS HV    (dB) -12 -12 -13 -13 -14 -14 -15 -15 -16 -16 R 2 = 0.73 -17 p < 0.0001 -17 -18 n = 253 -18 -19 -19 -20 -20 0 100 200 300 400 500 600 700 800 900 1000 1 10 100 1000 AGB (Mg ha -1 ) AGB (Mg ha -1 ) Mitchard, E. T. A., et al. 2009. Geophysical Research Letters

Biomass map – Mbam Djerem, Cameroon, 2007 Biomass (Mg ha -1 )

Railway Road Large towns 2000 2000 2006 2006 Mbam Djerem National Park

Radar wavelengths

La Selva, Costa Rica. Radar data at different wavelengths C-band L-band P-band

Biomass Maximum Wavelength sensitivity resolution Short Low High Long Low High

Radar satellites Typ ypical ma maximum Ban Band Wavele length reso esolu lutio ion Satell Sa llit ites from fr om orbi orbit Ter erraSAR-X (20 (2007-) X-band 2.5-3.75 cm ~1 m Tan anDEM-X (20 (2010-) COS OSMO-SkyMed (20 (2007-) ERS-1 (1991-2000) ERS-2 (1995-2011) ASAR (2002-2012) C-ba band 3.75-7.5 cm ~3 m RAD RADARSAT 1 (1995 1995-) RADARSAT 2 (20 RAD (2007-) Sen Sentin inel-1 (20 (2014-) S-ba band 7.5-15 cm ~6 m No NovaSAR (20 (2018-) JERS-1 (1992-1998) ALOS PALSAR (2007-2011) L-ba band 15-30 cm ~20 m ALOS-2 PALS AL ALSAR-2 (20 (2014-) SAOCOM (20 (2018-) NISAR (2019) P-band 70-130 cm ~50 m BIOMASS (2021)

Interferometry (tree height) A 2 B A 1 Phase center      ฀ ฀ ฀ H h v h g ground

ESA BIOMASS – 2021-25 Example data over Lope National Park, Gabon, produced using P-band aircraft campaign Biomass Level-2 Saatchi et al. (2011) Baccini et al. (2012) biomass t/ha biomass t/ha biomass t/ha 200m resolution (based on 1 km resolution 463 m resolution CESBIO algorithm using HV)

Can use radar/optical in combination – e.g. mapped peatlands in Amazon and Congo - Our research has increased known tropical peat stocks by ~35% through mapping peatland in Congo (Dargie et al. 2017 . Nature )

Conclusions - radar • Sees through clouds • Penetrates vegetation • Structural information on forests • But saturation – cannot map higher biomass forests or changes • No long-wavelength satellite currently – though BIOMASS launched 2021 • Data not normally free

So what can we do to map biomass and biomass changes in tropical forest? • Aircraft-based LiDAR works – but expensive • Satellite-based LiDAR very patchy • Radar data saturates  Wait for BIOMASS? – but only 2021-25, one-off mission  Can we do better with current technologies?

We are entering the Sentinel era • Sentinel 1 – C-band radar – 2 satellites • 4 TB data per day • 6/12/24 day repeat cycle • Sentinel 2 – Optical 10m - 2 satellites • 6 TB data per day • 5 day repeat cycle • Sentinel 3 – optical 300m (sea/land temperature and colour) – 1 satellite (3 from 2020) • global coverage every 2 days • 1.5 TB data per satellite per day • Can we use these satellites to map biomass? Or at least biomass change ?

FODEX Tropical Forest Degradation Experiment 42